Design and analysis of 2D sub-optimum filters for sharpening interpolated satellite images

The resolution of satellite images is controlled by the number of imaging sensors on board the satellite. Once the satellite is launched then this resolution cannot be changed during the lifetime of the satellite. The only way to increase the resolution of the images is by using super-resolution techniques. This paper deals with the design and analysis of 2D filters for improving the resolution of interpolated satellite images. The images are reduced first by a certain factor and then interpolated back to the original size. Linear phase 2D filters are designed to optimize the mean squared error (MSE) between the interpolated and the original satellite image. Then the satellite image is enlarged by the same factor and the 2D filter is used to sharpen the image. The performance of the new sub-optimum filters was assessed by using the peak signal to noise ratio (PSNR) and the structure similarity index measure (SSIM) on a variety of satellite images. It has been found that this method yields better results than using standard sharpening filters and very close to the optimum case when the resolution of the enlarged image is known.